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摘要: 为优化城市道路网络布局, 以若干个城市高德地图实时路况的交通出行数据为基础, 研究了主干路、次干路、支路等不同等级道路的路段状态相关性, 设置了考虑相关性的道路间距, 计算了城市路网密度与等级级配。分析结果表明: 主干路和主干路、主干路和次干路、主干路和支路相关单元的平行距离均服从对数正态分布, 平行距离的85%分位值分别为2.4、1.0、0.9km; 主干路间距取为2.4~2.5km, 主干路间设置2条次干路, 次干路与主干路的间距为0.8~1.2km, 支路间距为0.2km或0.3km; 次干路和支路对应平行距离的85%分位值比较接近, 实际路网中次干路和支路的功能区分尚不明确。所得路网结构布局符合相关性、路网密度与等级级配的要求, 可为路网规划提供参考。Abstract: In order to optimize the urban road network layout, based on the traffic trip data of several cities from AMAP real-time traffic road condition, the correlations of road section states with different road grades, including major road, minor road and branch road, were studied.The distances between roads considering the correlations were set.Urban road network density and grade proportion were calculated.Analysis result shows that parallel distances of related units between major roads, between major road and minor road and between major road and branch road, all obey log-normal distribution.The 85 percentile values of parallel distances are 2.4, 1.0 and 0.9 km, respectively.The distance between major roads is set as 2.4-2.5 km.2 minor roads are set between major roads.The distance between minor road and major road is 0.8-1.2 km, and the distance between branch roads is 0.2 km or 0.3 km.The corresponding 85 percentile values of parallel distances for minor road and branch road are relatively close, which shows that the functional distinction of minor road and branch road in actual road network is not clear.The proposed road network structure layout meets the requirements of correlation, road network density and grade proportion.It can provide reference for road network planning.
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图 2 主干路和主干路相关单元平行距离的概率密度
Figure 2. Probability density of parallel distance between related units of major roads
图 3 主干路和次干路相关单元平行距离的概率密度
Figure 3. Probability density of parallel distance between related units of major road and minor road
图 4 主干路和支路相关单元平行距离的概率密度
Figure 4. Probability density of parallel distance between related units of major road and branch road
表 4 不同等级道路间距的推荐值
Table 4. Recommended values of distances between roads withdifferent grades
Km 
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